Wang, Weiren and Zhou, Mai (1995): Iterative Least Squares Estimator of Binary Choice Models: a SemiParametric Approach.

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Abstract
Most existing semiparametric estimation procedures for binary choice models are based on the maximum score, maximum likelihood, or nonlinear least squares principles. These methods have two problems. They are difficult to compute and they may result in multiple local optima because they require optimizing nonlinear objective functions which may not be unimode. These problems are exacerbated when the number of explanatory variables increases or the sample size is large (Manski, 1975, 1985; Manski and Thompson, 1986; Cosslett, 1983; Ichimura, 1993; Horowitz, 1992; and Klein and Spady, 1993).
In this paper, we propose an easytocompute semiparametric estimator for binary choice models. The proposed method takes a completely different approach from the existing methods. The method is based on a semiparametric interpretation of the Expectation and Maximization (EM) principle (Dempster et al, 1977) and the least squares approach. By using the least squares method, the proposed method computes quickly and is immune to the problem of multiple local maxima. Furthermore, the computing time is not dramatically affected by the number of explanatory variables. The method compares favorably with other existing semiparametric methods in our Monte Carlo studies. The simulation results indicate that the proposed estimator is, 1) easytocompute and fast, 2) insensitive to initial estimates, 3) appears to be root(n)consistent and asymptotically normal, and, 4) better than other semiparametric estimators in terms of finite sample performances. The distinct advantages of the proposed method offer good potential for practical applications of semiparametric estimation of binary choice models.
Item Type:  MPRA Paper 

Original Title:  Iterative Least Squares Estimator of Binary Choice Models: a SemiParametric Approach 
English Title:  Iterative Least Squares Estimator of Binary Choice Models: a SemiParametric Approach 
Language:  English 
Keywords:  Binary choice models, EM algorithm, least squares method, semiparametric estimation. 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C25  Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities 
Item ID:  46981 
Depositing User:  Weiren Wang 
Date Deposited:  16 May 2013 06:31 
Last Modified:  28 Sep 2019 15:03 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/46981 